3 research outputs found

    How Does the Authenticity in an Online Review Affect Its Helpfulness? A Decision Tree Induction Theory Development Approach

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    Drawing on multi-dimensionality of authenticity, this study focuses on the role of two distinct authenticities: nominal and expressive. We propose that the type of authenticity in a review will vary based on the reviews’ lexical density (word level) and breadth (sentence level). Using the decision tree induction approach, the main and interaction effects of the dimensions and forms of authenticity are examined for their effect on review helpfulness. The preliminary analysis of 470 reviews demonstrate that the lexical density form of expressive authenticity is a predominant predictor of online review helpfulness. Additionally, the effects of expressive authenticity depth, nominal authenticity breadth and depth on online review helpfulness, vary based on the expressive breadth. The decision tree induction approach provides new theoretical insights that extends the frontiers of authenticity and practical implications on online review helpfulness

    Essays on the Influence of Review and Reviewer Attributes on Online Review Helpfulness: Attribution Theory Perspective

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    With the emergence of digital technology and the increasing availability of information on the internet, customers rely heavily on online reviews to inform their purchasing decisions. However, not all online reviews are helpful, and the factors that contribute to their helpfulness are complex and multifaceted. This dissertation addresses this gap in the literature by examining the antecedents that determine online review helpfulness using attribution theory. The dissertation consists of three essays. The first essay examines the impact of authenticity (review attribute) on review helpfulness, showing that the expressive authenticity of a review enhances its helpfulness. The second essay investigates the relationship between the reviewer attributes i.e., motivation, activity, and goals in online reviews. The study employs various machine learning techniques to investigate the influence of these factors on reviewers\u27 goal attainment. The third essay explores how the reviewer attributes are related to the helpfulness of online reviews. The dissertation offers significant theoretical and practical implications. Theoretically, the dissertation provides new insights into novel review and reviewer attributes. The study proposes a taxonomy of online reviews using means-ends fusion theory offering a framework for understanding the relationships between different components of online reviewer attributes and their contribution to the attainment of specific goals, such as emotional satisfaction. The study also highlights the importance of understanding the motivations and activities of online reviewers in predicting emotional satisfaction and the conditional effects of complaining behavior on emotional satisfaction. The findings inform review platform owners, business owners, reviewers, and prospective consumers in decision-making through helpful reviews. To review platform owners, the findings help segregate helpful reviews from the humongous number of reviews by determining the authenticity of the review. To business owners, the findings can help in understanding consumer behavior and taking necessary actions to provide better service to their customers. To reviewers, this dissertation can act as a guideline to write helpful reviews and to determine their helpfulness. Finally, to consumers or review readers, this dissertation provides an understanding of helpful reviews, thus allowing them to take product or service purchase decisions
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